{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "手部关键点检测 \n",
    "* 如果使用该数据集并发布相关项目或网络资源文章等，请讲述其数据集的出处 \"https://codechina.csdn.net/EricLee/handpose_x\"\n",
    "\n",
    "由pytorch移植到PaddlePaddle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 手部关键点检测\n",
    "\n",
    "\n",
    "找出手部21个关键点。\n",
    "\n",
    "# 一、项目背景\n",
    "\n",
    "第一次接触人工智能也是从人脸识别开始的，机缘巧合之下找到别人做的挺好的项目，不过是Pytorch的，想着既然在用Paddle，就移植过来了。\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/bbfad2c8db1741c8b032aa2c0edea3ec00c8033d457e4126b4d41710d18bb681)\n",
    "\n",
    "\n",
    "# 二、数据集简介\n",
    "[手部数据集](https://aistudio.baidu.com/aistudio/datasetdetail/79986)\n",
    "该数据集包括网络图片及数据集Large-scale Multiview 3D Hand Pose Dataset筛选动作重复度低的部分图片，其官网地址 http://www.rovit.ua.es/dataset/mhpdataset/ 数据集制作者Eric.Lee2021，项目网站https://codechina.csdn.net/EricLee/handpose_x 如有侵权请联系删除\n",
    "共49062张手部图片，并有json标注文件。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.数据加载和预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* 先导入图像处理、数据预处理等相关函数，还有库。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class LoadImagesAndLabels(paddle.io.Dataset):\r\n",
    "    def __init__(self, train_path,mode = 'train', img_size=(224,224), flag_agu = False,fix_res = True,vis = False):\r\n",
    "        self.mode = mode\r\n",
    "        # vis = True\r\n",
    "        print('img_size (height,width) : ',img_size[0],img_size[1])\r\n",
    "        print(\"train_path : {}\".format(train_path))\r\n",
    "\r\n",
    "        path = train_path\r\n",
    "\r\n",
    "        file_list = []\r\n",
    "        hand_anno_list = []\r\n",
    "        idx = 0\r\n",
    "        for f_ in os.listdir(path):\r\n",
    "            if \".jpg\" in f_:\r\n",
    "                img_path = path +f_\r\n",
    "                label_path = img_path.replace('.jpg','.json')\r\n",
    "                if not os.path.exists(label_path):\r\n",
    "                    continue\r\n",
    "\r\n",
    "                f = open(label_path, encoding='utf-8')#读取 json文件\r\n",
    "                hand_dict_ = json.load(f)\r\n",
    "                f.close()\r\n",
    "                if len(hand_dict_)==0:\r\n",
    "                    continue\r\n",
    "\r\n",
    "                hand_dict_ = hand_dict_[\"info\"]\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "                    \r\n",
    "                if len(hand_dict_)>0:\r\n",
    "                    for msg in hand_dict_:\r\n",
    "                        bbox = msg[\"bbox\"]\r\n",
    "                        pts = msg[\"pts\"]\r\n",
    "                        file_list.append(img_path)\r\n",
    "                        hand_anno_list.append((bbox,pts))\r\n",
    "                        idx += 1\r\n",
    "                        print(\"  hands num : {}\".format(idx),end = \"\\r\")\r\n",
    "\r\n",
    "        self.files = file_list\r\n",
    "        self.hand_anno_list = hand_anno_list\r\n",
    "        self.img_size = img_size\r\n",
    "        self.flag_agu = flag_agu\r\n",
    "        # self.fix_res = fix_res\r\n",
    "        self.vis = vis\r\n",
    "\r\n",
    "    def __len__(self):\r\n",
    "        return len(self.files)\r\n",
    "\r\n",
    "    def __getitem__(self, index):\r\n",
    "        img_path = self.files[index]\r\n",
    "        bbox,pts = self.hand_anno_list[index]\r\n",
    "        img = cv2.imread(img_path)  # BGR\r\n",
    "        #-------------------------------------\r\n",
    "        x1,y1,x2,y2 = int(bbox[0]),int(bbox[1]),int(bbox[2]),int(bbox[3])\r\n",
    "\r\n",
    "        pts_ = []\r\n",
    "\r\n",
    "        x_max = -65535\r\n",
    "        y_max = -65535\r\n",
    "        x_min = 65535\r\n",
    "        y_min = 65535\r\n",
    "        for i in range(21):\r\n",
    "            x_,y_ = pts[str(i)][\"x\"],pts[str(i)][\"y\"]\r\n",
    "            x_ += x1\r\n",
    "            y_ += y1\r\n",
    "            pts_.append([x_,y_])\r\n",
    "            x_min = x_ if x_min>x_ else x_min\r\n",
    "            y_min = y_ if y_min>y_ else y_min\r\n",
    "            x_max = x_ if x_max<x_ else x_max\r\n",
    "            y_max = y_ if y_max<y_ else y_max\r\n",
    "\r\n",
    "        if random.random() > 0.55:\r\n",
    "            offset_x = int((x_max-x_min)/8)\r\n",
    "            offset_y = int((y_max-y_min)/8)\r\n",
    "\r\n",
    "            pt_left = (x_min+random.randint(-offset_x,offset_x),(y_min+y_max)/2+random.randint(-offset_y,offset_y))\r\n",
    "            pt_right = (x_max+random.randint(-offset_x,offset_x),(y_min+y_max)/2+random.randint(-offset_y,offset_y))\r\n",
    "            angle_random = random.randint(-180,180)\r\n",
    "            scale_x = float(random.randint(12,33))/100.\r\n",
    "            hand_rot,pts_tor_landmarks,_ = hand_alignment_aug_fun(img,pt_left,pt_right,\r\n",
    "                facial_landmarks_n = pts_,\\\r\n",
    "                angle = angle_random,desiredLeftEye=(scale_x, 0.5),\r\n",
    "                desiredFaceWidth=self.img_size[0], desiredFaceHeight=None,draw_flag = False)\r\n",
    "            if self.vis:\r\n",
    "                pts_hand = {}\r\n",
    "                for ptk in range(21):\r\n",
    "                    xh,yh = pts_tor_landmarks[ptk][0],pts_tor_landmarks[ptk][1]\r\n",
    "                    pts_hand[str(ptk)] = {}\r\n",
    "                    pts_hand[str(ptk)] = {\r\n",
    "                        \"x\":xh,\r\n",
    "                        \"y\":yh,\r\n",
    "                        }\r\n",
    "\r\n",
    "                draw_bd_handpose(hand_rot,pts_hand,0,0)\r\n",
    "                cv2.namedWindow(\"hand_rotd\",0)\r\n",
    "                cv2.imshow(\"hand_rotd\",hand_rot)\r\n",
    "                cv2.waitKey(1)\r\n",
    "\r\n",
    "            img_ = hand_rot\r\n",
    "            pts_tor_landmarks_norm = []\r\n",
    "            for i in range(len(pts_tor_landmarks)):\r\n",
    "                x_ = float(pts_tor_landmarks[i][0])/float(self.img_size[0])\r\n",
    "                y_ = float(pts_tor_landmarks[i][1])/float(self.img_size[0])\r\n",
    "                pts_tor_landmarks_norm.append([x_,y_])\r\n",
    "\r\n",
    "        else:\r\n",
    "            w_ = max(abs(x_max-x_min),abs(y_max-y_min))\r\n",
    "            w_ = w_*(1.+float(random.randint(5,40))/100.)\r\n",
    "            x_mid = (x_max+x_min)/2\r\n",
    "            y_mid = (y_max+y_min)/2\r\n",
    "\r\n",
    "            x1,y1,x2,y2 = int(x_mid-w_/2.),int(y_mid-w_/2.),int(x_mid+w_/2.),int(y_mid+w_/2.)\r\n",
    "\r\n",
    "            x1 = np.clip(x1,0,img.shape[1]-1)\r\n",
    "            x2 = np.clip(x2,0,img.shape[1]-1)\r\n",
    "\r\n",
    "            y1 = np.clip(y1,0,img.shape[0]-1)\r\n",
    "            y2 = np.clip(y2,0,img.shape[0]-1)\r\n",
    "\r\n",
    "            img_ = img[y1:y2,x1:x2,:]\r\n",
    "\r\n",
    "            #-----------------\r\n",
    "            pts_tor_landmarks = []\r\n",
    "            pts_hand = {}\r\n",
    "            for ptk in range(21):\r\n",
    "                xh,yh = pts[str(ptk)][\"x\"],pts[str(ptk)][\"y\"]\r\n",
    "                xh = xh + bbox[0] -x1\r\n",
    "                yh = yh + bbox[1] -y1\r\n",
    "                pts_tor_landmarks.append([xh,yh])\r\n",
    "\r\n",
    "                pts_hand[str(ptk)] = {\r\n",
    "                    \"x\":xh,\r\n",
    "                    \"y\":yh,\r\n",
    "                    }\r\n",
    "            #----------------\r\n",
    "            if random.random() > 0.5: # 左右镜像\r\n",
    "                img_ = cv2.flip(img_,1)\r\n",
    "                pts_tor_landmarks = []\r\n",
    "                pts_hand = {}\r\n",
    "                for ptk in range(21):\r\n",
    "                    xh,yh = pts[str(ptk)][\"x\"],pts[str(ptk)][\"y\"]\r\n",
    "                    xh = xh + bbox[0] -x1\r\n",
    "                    yh = yh + bbox[1] -y1\r\n",
    "                    pts_tor_landmarks.append([img_.shape[1]-1-xh,yh])\r\n",
    "\r\n",
    "                    pts_hand[str(ptk)] = {\r\n",
    "                        \"x\":img_.shape[1]-1-xh,\r\n",
    "                        \"y\":yh,\r\n",
    "                        }\r\n",
    "\r\n",
    "            pts_tor_landmarks_norm = []\r\n",
    "            for i in range(len(pts_tor_landmarks)):\r\n",
    "                x_ = float(pts_tor_landmarks[i][0])/float(abs(x2-x1))\r\n",
    "                y_ = float(pts_tor_landmarks[i][1])/float(abs(y2-y1))\r\n",
    "                pts_tor_landmarks_norm.append([x_,y_])\r\n",
    "            #-----------------\r\n",
    "            if self.vis:\r\n",
    "                draw_bd_handpose(img_,pts_hand,0,0)\r\n",
    "\r\n",
    "            img_ = cv2.resize(img_, self.img_size, interpolation = random.randint(0,5))\r\n",
    "\r\n",
    "            if self.vis:\r\n",
    "                cv2.namedWindow(\"hand_zfx\",0)\r\n",
    "                cv2.imshow(\"hand_zfx\",img_)\r\n",
    "                cv2.waitKey(1)\r\n",
    "        #-------------------------------------\r\n",
    "        if self.flag_agu == True:\r\n",
    "            if random.random() > 0.5:\r\n",
    "                c = float(random.randint(80,120))/100.\r\n",
    "                b = random.randint(-10,10)\r\n",
    "                img_ = contrast_img(img_, c, b)\r\n",
    "        if self.flag_agu == True:\r\n",
    "            if random.random() > 0.9:\r\n",
    "                # print('agu hue ')\r\n",
    "                img_hsv=cv2.cvtColor(img_,cv2.COLOR_BGR2HSV)\r\n",
    "                hue_x = random.randint(-10,10)\r\n",
    "                # print(cc)\r\n",
    "                img_hsv[:,:,0]=(img_hsv[:,:,0]+hue_x)\r\n",
    "                img_hsv[:,:,0] =np.maximum(img_hsv[:,:,0],0)\r\n",
    "                img_hsv[:,:,0] =np.minimum(img_hsv[:,:,0],180)#范围 0 ~180\r\n",
    "                img_=cv2.cvtColor(img_hsv,cv2.COLOR_HSV2BGR)\r\n",
    "        if self.flag_agu == True:\r\n",
    "            if random.random() > 0.95:\r\n",
    "                img_ = img_agu_channel_same(img_)\r\n",
    "        if self.vis == True:\r\n",
    "            cv2.namedWindow('crop',0)\r\n",
    "            cv2.imshow('crop',img_)\r\n",
    "            cv2.waitKey(1)\r\n",
    "        img_ = img_.astype(np.float32)\r\n",
    "        img_ = (img_-128.)/256.\r\n",
    "        img_ = img_.transpose(2, 0, 1)\r\n",
    "\r\n",
    "\r\n",
    "        pts_tor_landmarks_norm = np.array(pts_tor_landmarks_norm).ravel()\r\n",
    "        # print(pts_tor_landmarks_norm)\r\n",
    "        return img_,pts_tor_landmarks_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "##  一些函数在lei.py中查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: 1: Syntax error: word unexpected (expecting \")\")\r\n"
     ]
    }
   ],
   "source": [
    "!sys.path.append('/home/aistudio/external-libraries')\r\n",
    "from lei import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* 读取图像集合，21个关键点，即42个坐标值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "img_size (height,width) :  256 256\n",
      "train_path : ./handpose_datasets/\n",
      "handpose done 49062\n",
      "len train datasets : 49062\n"
     ]
    }
   ],
   "source": [
    "\r\n",
    "dataset = LoadImagesAndLabels(\r\n",
    "            train_path= './handpose_datasets/', img_size=(256,256))\r\n",
    "\r\n",
    "print(\"handpose done\")\r\n",
    "\r\n",
    "print('len train datasets : %s' % (dataset.__len__()))\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* 用paddle读取，设置batchsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\r\n",
    "batch_size=128\r\n",
    "\r\n",
    "paddle.set_device('gpu')\r\n",
    "dataloader = paddle.io.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 二、数据展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[128, 3, 256, 256]\n",
      "[128, 42]\n",
      "Tensor(shape=[128, 42], dtype=float64, place=CUDAPinnedPlace, stop_gradient=True,\n",
      "       [[0.85869565, 0.55844156, 0.67391304, ..., 0.74025974, 0.22826087, 0.62337662],\n",
      "        [0.98305085, 0.29310345, 0.81355932, ..., 0.72413793, 0.44067797, 0.79310345],\n",
      "        [0.64673354, 0.23824689, 0.66594453, ..., 0.44146767, 0.32199272, 0.46842560],\n",
      "        ...,\n",
      "        [0.67307692, 0.92083333, 0.50961538, ..., 0.40416667, 0.32211538, 0.52916667],\n",
      "        [0.86060812, 0.35186532, 0.61032571, ..., 0.84669774, 0.42136501, 0.85119432],\n",
      "        [0.48291921, 0.76712372, 0.61374544, ..., 0.36236471, 0.58965742, 0.32765050]])\n"
     ]
    }
   ],
   "source": [
    "for batch_id, data in enumerate(dataloader()):\r\n",
    "    x_data = data[0]\r\n",
    "    y_data = data[1]\r\n",
    "    print(x_data.shape)\r\n",
    "    print(y_data.shape)\r\n",
    "    print(y_data)\r\n",
    "    break\r\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len hand_dict : 1\n",
      "[{'bbox': [0.0, 0.0, 0.0, 0.0], 'pts': {'0': {'x': 220, 'y': 66}, '1': {'x': 216, 'y': 117}, '2': {'x': 188, 'y': 141}, '3': {'x': 164, 'y': 149}, '4': {'x': 137, 'y': 165}, '5': {'x': 129, 'y': 102}, '6': {'x': 93, 'y': 106}, '7': {'x': 57, 'y': 109}, '8': {'x': 25, 'y': 109}, '9': {'x': 117, 'y': 86}, '10': {'x': 85, 'y': 90}, '11': {'x': 45, 'y': 94}, '12': {'x': 17, 'y': 98}, '13': {'x': 121, 'y': 74}, '14': {'x': 85, 'y': 78}, '15': {'x': 57, 'y': 86}, '16': {'x': 29, 'y': 90}, '17': {'x': 125, 'y': 66}, '18': {'x': 97, 'y': 74}, '19': {'x': 77, 'y': 82}, '20': {'x': 61, 'y': 82}}}]\n",
      "[0.0, 0.0, 0.0, 0.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path='./handpose_datasets/'\r\n",
    "for f_ in os.listdir(path):\r\n",
    "    if \".jpg\" in f_:\r\n",
    "        img_path = path + f_\r\n",
    "        label_path = img_path.replace('.jpg', '.json')\r\n",
    "        if not os.path.exists(label_path):\r\n",
    "            continue\r\n",
    "        img_ = cv2.imread(img_path)\r\n",
    "\r\n",
    "        f = open(label_path, encoding='utf-8')  # 读取 json文件\r\n",
    "        hand_dict_ = json.load(f)\r\n",
    "        f.close()\r\n",
    "\r\n",
    "        hand_dict_ = hand_dict_[\"info\"]\r\n",
    "        print(\"len hand_dict :\", len(hand_dict_))\r\n",
    "        print(hand_dict_)\r\n",
    "        if len(hand_dict_) > 0:\r\n",
    "            for msg in hand_dict_:\r\n",
    "                # print(msg)\r\n",
    "                bbox = msg[\"bbox\"]\r\n",
    "                pts = msg[\"pts\"]\r\n",
    "                # print()\r\n",
    "                print(bbox)\r\n",
    "                RGB = (random.randint(50, 255), random.randint(\r\n",
    "                    50, 255), random.randint(50, 255))\r\n",
    "                # plot_box(bbox, img_, color=(RGB),\r\n",
    "                #          label=\"hand\", line_thickness=3)\r\n",
    "                # draw_bd_handpose(img_, pts, bbox[0], bbox[1])\r\n",
    "\r\n",
    "                for k_ in pts.keys():\r\n",
    "                    cv2.circle(img_, (int(\r\n",
    "                        pts[k_]['x'] + bbox[0]), int(pts[k_]['y'] + bbox[1])), 3, (255, 50, 155), -1)\r\n",
    "\r\n",
    "            \r\n",
    "            plt.imshow(img_)\r\n",
    "            plt.show()\r\n",
    "            break  # 1111\r\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 三、模型选择和开发\n",
    "\n",
    "使用典型的ResNet50残差网络模型。代码结构在resnet50.py中"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* 设置学习率，模型，优化器参数,查看模型结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------\n",
      " Layer (type)       Input Shape          Output Shape         Param #    \n",
      "===========================================================================\n",
      "  Conv2D-160     [[1, 3, 256, 256]]   [1, 64, 128, 128]        9,408     \n",
      "BatchNorm2D-160 [[1, 64, 128, 128]]   [1, 64, 128, 128]         256      \n",
      "    ReLU-52     [[1, 64, 128, 128]]   [1, 64, 128, 128]          0       \n",
      "  MaxPool2D-4   [[1, 64, 128, 128]]    [1, 64, 64, 64]           0       \n",
      "  Conv2D-162     [[1, 64, 64, 64]]     [1, 64, 64, 64]         4,096     \n",
      "BatchNorm2D-162  [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "    ReLU-53      [[1, 256, 64, 64]]    [1, 256, 64, 64]          0       \n",
      "  Conv2D-163     [[1, 64, 64, 64]]     [1, 64, 64, 64]        36,864     \n",
      "BatchNorm2D-163  [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "  Conv2D-164     [[1, 64, 64, 64]]     [1, 256, 64, 64]       16,384     \n",
      "BatchNorm2D-164  [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024     \n",
      "  Conv2D-161     [[1, 64, 64, 64]]     [1, 256, 64, 64]       16,384     \n",
      "BatchNorm2D-161  [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024     \n",
      " Bottleneck-49   [[1, 64, 64, 64]]     [1, 256, 64, 64]          0       \n",
      "  Conv2D-165     [[1, 256, 64, 64]]    [1, 64, 64, 64]        16,384     \n",
      "BatchNorm2D-165  [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "    ReLU-54      [[1, 256, 64, 64]]    [1, 256, 64, 64]          0       \n",
      "  Conv2D-166     [[1, 64, 64, 64]]     [1, 64, 64, 64]        36,864     \n",
      "BatchNorm2D-166  [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "  Conv2D-167     [[1, 64, 64, 64]]     [1, 256, 64, 64]       16,384     \n",
      "BatchNorm2D-167  [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024     \n",
      " Bottleneck-50   [[1, 256, 64, 64]]    [1, 256, 64, 64]          0       \n",
      "  Conv2D-168     [[1, 256, 64, 64]]    [1, 64, 64, 64]        16,384     \n",
      "BatchNorm2D-168  [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "    ReLU-55      [[1, 256, 64, 64]]    [1, 256, 64, 64]          0       \n",
      "  Conv2D-169     [[1, 64, 64, 64]]     [1, 64, 64, 64]        36,864     \n",
      "BatchNorm2D-169  [[1, 64, 64, 64]]     [1, 64, 64, 64]          256      \n",
      "  Conv2D-170     [[1, 64, 64, 64]]     [1, 256, 64, 64]       16,384     \n",
      "BatchNorm2D-170  [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024     \n",
      " Bottleneck-51   [[1, 256, 64, 64]]    [1, 256, 64, 64]          0       \n",
      "  Conv2D-172     [[1, 256, 64, 64]]    [1, 128, 64, 64]       32,768     \n",
      "BatchNorm2D-172  [[1, 128, 64, 64]]    [1, 128, 64, 64]         512      \n",
      "    ReLU-56      [[1, 512, 32, 32]]    [1, 512, 32, 32]          0       \n",
      "  Conv2D-173     [[1, 128, 64, 64]]    [1, 128, 32, 32]       147,456    \n",
      "BatchNorm2D-173  [[1, 128, 32, 32]]    [1, 128, 32, 32]         512      \n",
      "  Conv2D-174     [[1, 128, 32, 32]]    [1, 512, 32, 32]       65,536     \n",
      "BatchNorm2D-174  [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048     \n",
      "  Conv2D-171     [[1, 256, 64, 64]]    [1, 512, 32, 32]       131,072    \n",
      "BatchNorm2D-171  [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048     \n",
      " Bottleneck-52   [[1, 256, 64, 64]]    [1, 512, 32, 32]          0       \n",
      "  Conv2D-175     [[1, 512, 32, 32]]    [1, 128, 32, 32]       65,536     \n",
      "BatchNorm2D-175  [[1, 128, 32, 32]]    [1, 128, 32, 32]         512      \n",
      "    ReLU-57      [[1, 512, 32, 32]]    [1, 512, 32, 32]          0       \n",
      "  Conv2D-176     [[1, 128, 32, 32]]    [1, 128, 32, 32]       147,456    \n",
      "BatchNorm2D-176  [[1, 128, 32, 32]]    [1, 128, 32, 32]         512      \n",
      "  Conv2D-177     [[1, 128, 32, 32]]    [1, 512, 32, 32]       65,536     \n",
      "BatchNorm2D-177  [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048     \n",
      " Bottleneck-53   [[1, 512, 32, 32]]    [1, 512, 32, 32]          0       \n",
      "  Conv2D-178     [[1, 512, 32, 32]]    [1, 128, 32, 32]       65,536     \n",
      "BatchNorm2D-178  [[1, 128, 32, 32]]    [1, 128, 32, 32]         512      \n",
      "    ReLU-58      [[1, 512, 32, 32]]    [1, 512, 32, 32]          0       \n",
      "  Conv2D-179     [[1, 128, 32, 32]]    [1, 128, 32, 32]       147,456    \n",
      "BatchNorm2D-179  [[1, 128, 32, 32]]    [1, 128, 32, 32]         512      \n",
      "  Conv2D-180     [[1, 128, 32, 32]]    [1, 512, 32, 32]       65,536     \n",
      "BatchNorm2D-180  [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048     \n",
      " Bottleneck-54   [[1, 512, 32, 32]]    [1, 512, 32, 32]          0       \n",
      "  Conv2D-181     [[1, 512, 32, 32]]    [1, 128, 32, 32]       65,536     \n",
      "BatchNorm2D-181  [[1, 128, 32, 32]]    [1, 128, 32, 32]         512      \n",
      "    ReLU-59      [[1, 512, 32, 32]]    [1, 512, 32, 32]          0       \n",
      "  Conv2D-182     [[1, 128, 32, 32]]    [1, 128, 32, 32]       147,456    \n",
      "BatchNorm2D-182  [[1, 128, 32, 32]]    [1, 128, 32, 32]         512      \n",
      "  Conv2D-183     [[1, 128, 32, 32]]    [1, 512, 32, 32]       65,536     \n",
      "BatchNorm2D-183  [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048     \n",
      " Bottleneck-55   [[1, 512, 32, 32]]    [1, 512, 32, 32]          0       \n",
      "  Conv2D-185     [[1, 512, 32, 32]]    [1, 256, 32, 32]       131,072    \n",
      "BatchNorm2D-185  [[1, 256, 32, 32]]    [1, 256, 32, 32]        1,024     \n",
      "    ReLU-60     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-186     [[1, 256, 32, 32]]    [1, 256, 16, 16]       589,824    \n",
      "BatchNorm2D-186  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "  Conv2D-187     [[1, 256, 16, 16]]   [1, 1024, 16, 16]       262,144    \n",
      "BatchNorm2D-187 [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096     \n",
      "  Conv2D-184     [[1, 512, 32, 32]]   [1, 1024, 16, 16]       524,288    \n",
      "BatchNorm2D-184 [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096     \n",
      " Bottleneck-56   [[1, 512, 32, 32]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-188    [[1, 1024, 16, 16]]    [1, 256, 16, 16]       262,144    \n",
      "BatchNorm2D-188  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "    ReLU-61     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-189     [[1, 256, 16, 16]]    [1, 256, 16, 16]       589,824    \n",
      "BatchNorm2D-189  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "  Conv2D-190     [[1, 256, 16, 16]]   [1, 1024, 16, 16]       262,144    \n",
      "BatchNorm2D-190 [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096     \n",
      " Bottleneck-57  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-191    [[1, 1024, 16, 16]]    [1, 256, 16, 16]       262,144    \n",
      "BatchNorm2D-191  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "    ReLU-62     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-192     [[1, 256, 16, 16]]    [1, 256, 16, 16]       589,824    \n",
      "BatchNorm2D-192  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "  Conv2D-193     [[1, 256, 16, 16]]   [1, 1024, 16, 16]       262,144    \n",
      "BatchNorm2D-193 [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096     \n",
      " Bottleneck-58  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-194    [[1, 1024, 16, 16]]    [1, 256, 16, 16]       262,144    \n",
      "BatchNorm2D-194  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "    ReLU-63     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-195     [[1, 256, 16, 16]]    [1, 256, 16, 16]       589,824    \n",
      "BatchNorm2D-195  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "  Conv2D-196     [[1, 256, 16, 16]]   [1, 1024, 16, 16]       262,144    \n",
      "BatchNorm2D-196 [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096     \n",
      " Bottleneck-59  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-197    [[1, 1024, 16, 16]]    [1, 256, 16, 16]       262,144    \n",
      "BatchNorm2D-197  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "    ReLU-64     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-198     [[1, 256, 16, 16]]    [1, 256, 16, 16]       589,824    \n",
      "BatchNorm2D-198  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "  Conv2D-199     [[1, 256, 16, 16]]   [1, 1024, 16, 16]       262,144    \n",
      "BatchNorm2D-199 [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096     \n",
      " Bottleneck-60  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-200    [[1, 1024, 16, 16]]    [1, 256, 16, 16]       262,144    \n",
      "BatchNorm2D-200  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "    ReLU-65     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-201     [[1, 256, 16, 16]]    [1, 256, 16, 16]       589,824    \n",
      "BatchNorm2D-201  [[1, 256, 16, 16]]    [1, 256, 16, 16]        1,024     \n",
      "  Conv2D-202     [[1, 256, 16, 16]]   [1, 1024, 16, 16]       262,144    \n",
      "BatchNorm2D-202 [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096     \n",
      " Bottleneck-61  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0       \n",
      "  Conv2D-204    [[1, 1024, 16, 16]]    [1, 512, 16, 16]       524,288    \n",
      "BatchNorm2D-204  [[1, 512, 16, 16]]    [1, 512, 16, 16]        2,048     \n",
      "    ReLU-66      [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0       \n",
      "  Conv2D-205     [[1, 512, 16, 16]]     [1, 512, 8, 8]       2,359,296   \n",
      "BatchNorm2D-205   [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     \n",
      "  Conv2D-206      [[1, 512, 8, 8]]     [1, 2048, 8, 8]       1,048,576   \n",
      "BatchNorm2D-206  [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192     \n",
      "  Conv2D-203    [[1, 1024, 16, 16]]    [1, 2048, 8, 8]       2,097,152   \n",
      "BatchNorm2D-203  [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192     \n",
      " Bottleneck-62  [[1, 1024, 16, 16]]    [1, 2048, 8, 8]           0       \n",
      "  Conv2D-207     [[1, 2048, 8, 8]]      [1, 512, 8, 8]       1,048,576   \n",
      "BatchNorm2D-207   [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     \n",
      "    ReLU-67      [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0       \n",
      "  Conv2D-208      [[1, 512, 8, 8]]      [1, 512, 8, 8]       2,359,296   \n",
      "BatchNorm2D-208   [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     \n",
      "  Conv2D-209      [[1, 512, 8, 8]]     [1, 2048, 8, 8]       1,048,576   \n",
      "BatchNorm2D-209  [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192     \n",
      " Bottleneck-63   [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0       \n",
      "  Conv2D-210     [[1, 2048, 8, 8]]      [1, 512, 8, 8]       1,048,576   \n",
      "BatchNorm2D-210   [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     \n",
      "    ReLU-68      [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0       \n",
      "  Conv2D-211      [[1, 512, 8, 8]]      [1, 512, 8, 8]       2,359,296   \n",
      "BatchNorm2D-211   [[1, 512, 8, 8]]      [1, 512, 8, 8]         2,048     \n",
      "  Conv2D-212      [[1, 512, 8, 8]]     [1, 2048, 8, 8]       1,048,576   \n",
      "BatchNorm2D-212  [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192     \n",
      " Bottleneck-64   [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0       \n",
      "  AvgPool2D-4    [[1, 2048, 8, 8]]     [1, 2048, 1, 1]           0       \n",
      "   Dropout-4        [[1, 2048]]           [1, 2048]              0       \n",
      "   Linear-4         [[1, 2048]]            [1, 42]            86,058     \n",
      "===========================================================================\n",
      "Total params: 23,647,210\n",
      "Trainable params: 23,540,970\n",
      "Non-trainable params: 106,240\n",
      "---------------------------------------------------------------------------\n",
      "Input size (MB): 0.75\n",
      "Forward/backward pass size (MB): 341.53\n",
      "Params size (MB): 90.21\n",
      "Estimated Total Size (MB): 432.49\n",
      "---------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from resnet50 import resnet50\r\n",
    "\r\n",
    "model_ = resnet50( num_classes=42, img_size=256)\r\n",
    "model_1=paddle.Model(model_)\r\n",
    "model_1.summary((1,3, 256, 256))\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 模型训练\n",
    "##  模型训练准备\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "init_lr = 0.0001\r\n",
    "# optim = paddle.optimizer.Adam(learning_rate=1e-3, parameters=model.parameters())\r\n",
    "# criterion = paddle.nn.MSELoss(reduction='mean')\r\n",
    "# opt = paddle.optimizer.SGD(parameters=model_.parameters(), learning_rate=init_lr, weight_decay=1e-6)# 优化器初始化\r\n",
    "opt = paddle.optimizer.Adam(beta1=0.9,beta2=0.99,learning_rate=init_lr, weight_decay=paddle.regularizer.L2Decay(1e-6), parameters=model_.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* 可选-加载上一次的训练参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "version='848'\r\n",
    "layer_state_dict = paddle.load(\"848resnet_50-model_epoch-9.pdparams\") \r\n",
    "opt_state_dict = paddle.load(\"848resnet_50-model_epoch-9-opt.pdopt\")\r\n",
    "\r\n",
    "model_.set_state_dict(layer_state_dict) \r\n",
    "opt.set_state_dict(opt_state_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "##  一些参数初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "optimizer =opt\r\n",
    "step = 0\r\n",
    "idx = 0\r\n",
    "\r\n",
    "acc=0\r\n",
    "# 变量初始化\r\n",
    "best_loss = np.inf\r\n",
    "loss_mean = 0.  # 损失均值\r\n",
    "loss_idx = 0.  # 损失计算计数器\r\n",
    "flag_change_lr_cnt = 0  # 学习率更新计数器\r\n",
    "\r\n",
    "epochs=1\r\n",
    "lr_decay=0.1\r\n",
    "\r\n",
    "model='resnet_50'\r\n",
    "iters=[]\r\n",
    "losses=[]\r\n",
    "img_size=(256,256)\r\n",
    "seed=126673\r\n",
    "model_exp='./model_exp/'\r\n",
    "epochs_loss_dict = {}\r\n",
    "\r\n",
    "data_num=dataset.__len__()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    "##  开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch 0 ------>>>\n",
      "\n",
      "updated!\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:641: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:239: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.float64, the right dtype will convert to paddle.float32\n",
      "  format(lhs_dtype, rhs_dtype, lhs_dtype))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(shape=[42], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
      "       [0.81568670, 0.72247696, 0.76796705, 0.55653471, 0.67624116, 0.44409657, 0.59869903, 0.34895328, 0.54227418, 0.23683736, 0.43922403, 0.61781067, 0.32964197, 0.53953612, 0.24744868, 0.45615155, 0.19834349, 0.39174148, 0.39054063, 0.66006911, 0.24746193, 0.56221557, 0.17731358, 0.47306293, 0.13584581, 0.41364926, 0.37409985, 0.67049408, 0.24391551, 0.58375335, 0.16405225, 0.51065618, 0.11335891, 0.43194145, 0.38906375, 0.68481058, 0.27687880, 0.61488730, 0.21269795, 0.54657382, 0.16437519, 0.47079322])\n",
      "\n",
      "abc\n",
      "\n",
      "Tensor(shape=[42], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n",
      "       [0.90335873, 0.63544346, 0.77430458, 0.49378266, 0.64351307, 0.41631867, 0.58388223, 0.34342064, 0.49661128, 0.21873156, 0.42351236, 0.57845482, 0.26704791, 0.60414222, 0.18369685, 0.52972220, 0.12406602, 0.45682418, 0.39783942, 0.68160621, 0.23310448, 0.68770864, 0.12624856, 0.57129005, 0.04441949, 0.47314981, 0.43962263, 0.69857793, 0.28793952, 0.61262808, 0.20458846, 0.53820806, 0.13255188, 0.43593257, 0.53885414, 0.67398180, 0.43744010, 0.58411206, 0.39195236, 0.47639469, 0.34624926, 0.40915390])\n",
      "  2021-08-02 10:16:23 - resnet_50 - epoch [0/1] (0/383): Mean Loss : 0.087039 - Loss: 0.087039 - Acc: 0  lr : 0.00001000  bs : 128  img_size: 256 x 256  best_loss: inf\n",
      "  2021-08-02 10:16:53 - resnet_50 - epoch [0/1] (50/383): Mean Loss : 0.086131 - Loss: 0.086682 - Acc: 0  lr : 0.00001000  bs : 128  img_size: 256 x 256  best_loss: inf\n",
      "  2021-08-02 10:17:22 - resnet_50 - epoch [0/1] (100/383): Mean Loss : 0.085960 - Loss: 0.085094 - Acc: 0  lr : 0.00001000  bs : 128  img_size: 256 x 256  best_loss: inf\n",
      "  2021-08-02 10:17:52 - resnet_50 - epoch [0/1] (150/383): Mean Loss : 0.086492 - Loss: 0.088189 - Acc: 0  lr : 0.00001000  bs : 128  img_size: 256 x 256  best_loss: inf\n",
      "Tensor(shape=[42], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
      "       [0.41543356, 0.79113048, 0.33238560, 0.62696302, 0.32891655, 0.50249434, 0.37842923, 0.40471300, 0.42329335, 0.32895547, 0.36839235, 0.38149506, 0.44400030, 0.25693578, 0.48210388, 0.26805049, 0.49246210, 0.29820290, 0.46383864, 0.40156636, 0.55904210, 0.27413404, 0.57834023, 0.30219087, 0.57476163, 0.34960875, 0.55627382, 0.44487044, 0.64684355, 0.32576948, 0.65400779, 0.35735500, 0.63996762, 0.40331548, 0.64509428, 0.50242263, 0.71890318, 0.40932605, 0.72964275, 0.41962716, 0.71533394, 0.44651154])\n",
      "\n",
      "abc\n",
      "\n",
      "Tensor(shape=[42], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n",
      "       [0.43415686, 0.83959380, 0.33666223, 0.65662801, 0.30843776, 0.53995143, 0.32433962, 0.42963560, 0.35286325, 0.30987837, 0.38426810, 0.40449178, 0.44270610, 0.31156827, 0.50422476, 0.27539287, 0.55620231, 0.30540697, 0.45989899, 0.42665466, 0.54517072, 0.30381678, 0.59088724, 0.29914594, 0.60658967, 0.34645265, 0.55441268, 0.47406109, 0.61603106, 0.35907442, 0.65230618, 0.34178181, 0.66959879, 0.37805693, 0.64892637, 0.52146751, 0.68689140, 0.41433205, 0.71372513, 0.38441767, 0.71998615, 0.41910261])\n",
      "  2021-08-02 10:18:21 - resnet_50 - epoch [0/1] (200/383): Mean Loss : 0.086650 - Loss: 0.090172 - Acc: 0  lr : 0.00001000  bs : 128  img_size: 256 x 256  best_loss: inf\n",
      "  2021-08-02 10:18:51 - resnet_50 - epoch [0/1] (250/383): Mean Loss : 0.086442 - Loss: 0.085069 - Acc: 0  lr : 0.00001000  bs : 128  img_size: 256 x 256  best_loss: inf\n",
      "  2021-08-02 10:19:20 - resnet_50 - epoch [0/1] (300/383): Mean Loss : 0.086301 - Loss: 0.083891 - Acc: 0  lr : 0.00001000  bs : 128  img_size: 256 x 256  best_loss: inf\n",
      "  2021-08-02 10:19:50 - resnet_50 - epoch [0/1] (350/383): Mean Loss : 0.086274 - Loss: 0.088587 - Acc: 0  lr : 0.00001000  bs : 128  img_size: 256 x 256  best_loss: inf\n"
     ]
    }
   ],
   "source": [
    "\r\n",
    "for epoch in range(0, epochs):\r\n",
    "\r\n",
    "    print('\\nepoch %d ------>>>' % epoch)\r\n",
    "    model_.train()\r\n",
    "    # 学习率更新策略\r\n",
    "    if epoch%20==0:\r\n",
    "        init_lr = init_lr * lr_decay\r\n",
    "        optimizer.set_lr(init_lr)\r\n",
    "        print('\\nupdated!\\n')\r\n",
    "    if loss_mean != 0.:\r\n",
    "        if best_loss > (loss_mean / loss_idx):\r\n",
    "            # flag_change_lr_cnt = 0\r\n",
    "            best_loss = (loss_mean / loss_idx)\r\n",
    "            print(\"\\nbest_loss:{}\\n\".format(best_loss))\r\n",
    "            print(\"\\nflag_change_lr_cnt:{}\\n\".format(flag_change_lr_cnt))\r\n",
    "        else:\r\n",
    "            flag_change_lr_cnt += 1\r\n",
    "            print(\"\\nflag_change_lr_cnt:{}\\n\".format(flag_change_lr_cnt))\r\n",
    "            if flag_change_lr_cnt > 3:\r\n",
    "                init_lr = init_lr * lr_decay\r\n",
    "                optimizer.set_lr(init_lr)\r\n",
    "                print('\\nupdata\\n')\r\n",
    "                # set_learning_rate(optimizer, init_lr)\r\n",
    "                flag_change_lr_cnt = 0\r\n",
    "\r\n",
    "    loss_mean = 0.  # 损失均值\r\n",
    "    loss_idx = 0.  # 损失计算计数器\r\n",
    "\r\n",
    "    for i, (imgs_, pts_) in enumerate(dataloader):\r\n",
    "\r\n",
    "\r\n",
    "        output = model_(imgs_)\r\n",
    "        if i%200==0:\r\n",
    "            print(output[0])\r\n",
    "            print('\\nabc\\n')\r\n",
    "            print(pts_[0])\r\n",
    "        loss = got_total_wing_loss(output, pts_)\r\n",
    "\r\n",
    "        # m=paddle.metric.Accuracy()\r\n",
    "        # correct = m.compute(output[1]*100, pts_[1]*100)\r\n",
    "        # m.update(correct)\r\n",
    "        # acc = m.accumulate()\r\n",
    "        # output=paddle.to_tensor(output)\r\n",
    "        # pts_=paddle.to_tensor(pts_)\r\n",
    "        # acc= paddle.nn.functional.cosine_similarity(output[0], pts_[1], axis=1)\r\n",
    "        iters.append(i + epoch*data_num)\r\n",
    "        losses.append(loss)\r\n",
    "\r\n",
    "        loss_mean += loss.item()\r\n",
    "        loss_idx += 1.\r\n",
    "        if i % 50 == 0:\r\n",
    "            loc_time = time.strftime(\r\n",
    "                \"%Y-%m-%d %H:%M:%S\", time.localtime())\r\n",
    "            print('  %s - %s - epoch [%s/%s] (%s/%s):' % (loc_time, model, epoch, epochs, i, int(data_num / batch_size)),\r\n",
    "                    'Mean Loss : %.6f - Loss: %.6f - Acc: %d' % (\r\n",
    "                        loss_mean / loss_idx, loss.item(),acc),\r\n",
    "                    ' lr : %.8f' % init_lr, ' bs :', batch_size,\r\n",
    "                    ' img_size: %s x %s' % (img_size[0], img_size[1]), ' best_loss: %.6f' % best_loss)\r\n",
    "        # 计算梯度\r\n",
    "        loss.backward()\r\n",
    "        # 优化器对模型参数更新\r\n",
    "        optimizer.step()\r\n",
    "        # 优化器梯度清零\r\n",
    "        optimizer.clear_grad()\r\n",
    "        step += 1\r\n",
    "    \r\n",
    "    paddle.save(model_.state_dict(), model_exp + version+\r\n",
    "            '{}-model_epoch-{}.pdparams'.format(model, epoch))\r\n",
    "    paddle.save(opt.state_dict(), model_exp  + version+'{}-model_epoch-{}-opt.pdopt'.format(model, epoch))\r\n",
    "\r\n",
    "\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  if isinstance(obj, collections.Iterator):\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return list(data) if isinstance(data, collections.MappingView) else data\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画出训练过程中Loss的变化曲线\r\n",
    "plt.figure()\r\n",
    "plt.title(\"train loss\", fontsize=24)\r\n",
    "plt.xlabel(\"iter\", fontsize=14)\r\n",
    "plt.ylabel(\"loss\", fontsize=14)\r\n",
    "plt.plot(iters, losses,color='red',label='train loss') \r\n",
    "plt.grid()\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# !mv model_exp/848resnet_50-model_epoch-9.pdparams ./\r\n",
    "# !mv model_exp/848resnet_50-model_epoch-9-opt.pdopt ./"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# !rm ./model_exp/*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 四.模型预测及效果展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abc\n",
      "\n",
      "abc\n",
      "\n",
      "abc\n",
      "\n",
      "abc\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  if isinstance(obj, collections.Iterator):\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return list(data) if isinstance(data, collections.MappingView) else data\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lei import *\r\n",
    "from resnet50 import resnet50\r\n",
    "model_ = resnet50(num_classes=42, img_size=256)\r\n",
    "\r\n",
    "file_path='2021-02-01_13-52-45_4.jpg'  #要预测的图\r\n",
    "model_.eval()  # 设置为前向推断模式\r\n",
    "\r\n",
    "model_path = '848resnet_50-model_epoch-9.pdparams' #训练出的模型\r\n",
    "chkpt = paddle.load(model_path)\r\n",
    "\r\n",
    "\r\n",
    "ss = model_.set_state_dict(chkpt)\r\n",
    "paddle.no_grad()\r\n",
    "img = cv2.imread(file_path)\r\n",
    "\r\n",
    "# cv2.imshow('frame', frame)\r\n",
    "img_width = img.shape[1]\r\n",
    "img_height = img.shape[0]\r\n",
    "# 输入图片预处理\r\n",
    "img_ = cv2.resize(\r\n",
    "    img, (256, 256), interpolation=cv2.INTER_CUBIC)\r\n",
    "img_ = img_.astype(np.float32)\r\n",
    "img_ = (img_ - 128.) / 256.\r\n",
    "\r\n",
    "img_ = img_.transpose(2, 0, 1)\r\n",
    "# img_ = torch.from_numpy(img_)\r\n",
    "img_ = paddle.to_tensor(img_)\r\n",
    "# print(img_)\r\n",
    "img_ = img_.unsqueeze_(0)\r\n",
    "\r\n",
    "# if use_cuda:\r\n",
    "#     img_ = img_.cuda()  # (bs, 3, h, w)\r\n",
    "pre_ = model_(img_)  # 模型推理\r\n",
    "output = pre_.numpy()\r\n",
    "# print(output)\r\n",
    "# output = pre_.cpu().detach().numpy()\r\n",
    "output = np.squeeze(output)\r\n",
    "\r\n",
    "pts_hand = {}  # 构建关键点连线可视化结构\r\n",
    "for i in range(int(output.shape[0] / 2)):\r\n",
    "    x = (output[i * 2 + 0] * float(img_width))\r\n",
    "    y = (output[i * 2 + 1] * float(img_height))\r\n",
    "\r\n",
    "    pts_hand[str(i)] = {}\r\n",
    "    pts_hand[str(i)] = {\r\n",
    "        \"x\": x,\r\n",
    "        \"y\": y,\r\n",
    "    }\r\n",
    "draw_bd_handpose(img, pts_hand, 0, 0)  # 绘制关键点连线\r\n",
    "\r\n",
    "# ------------- 绘制关键点\r\n",
    "for i in range(int(output.shape[0] / 2)):\r\n",
    "    x = (output[i * 2 + 0] * float(img_width))\r\n",
    "    y = (output[i * 2 + 1] * float(img_height))\r\n",
    "\r\n",
    "    cv2.circle(img, (int(x), int(y)), 3, (255, 50, 60), -1)\r\n",
    "    cv2.circle(img, (int(x), int(y)), 1, (255, 150, 180), -1)\r\n",
    "plt.imshow(img)\r\n",
    "plt.show()\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 五、总结\n",
    "##  如此就算完成了手部关键点检测了。只能算是个搬运工，但是对于Paddle确实是更熟悉了，中间一直查paddle的api文档，确实好用。\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/6cf64692c4734aa7a0f0e5d57ab7da4c179349fb2bcf4b1fbadb0fdd9f377166)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 以下部分是使用paddlehub部署，要先建立inferrence文件夹等。\n",
    "先看教程[帅小伙都能看懂的Hub模型转换](https://aistudio.baidu.com/aistudio/projectdetail/2259744)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "[2021-08-06 12:21:30,712] [    INFO] - Successfully uninstalled mnist_predict\n",
      "W0806 12:21:31.534155  6579 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1\n",
      "W0806 12:21:31.540107  6579 device_context.cc:422] device: 0, cuDNN Version: 7.6.\n",
      "[2021-08-06 12:21:35,351] [    INFO] - Successfully installed mnist_predict-1.0.0\n"
     ]
    }
   ],
   "source": [
    "! hub install inferrence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "[2021-08-06 12:41:07,663] [    INFO] - Successfully uninstalled mnist_predict\n",
      "W0806 12:41:08.517396  8753 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1\n",
      "W0806 12:41:08.523392  8753 device_context.cc:422] device: 0, cuDNN Version: 7.6.\n",
      "[2021-08-06 12:41:12,482] [    INFO] - Successfully installed mnist_predict-1.0.0\n",
      "forward\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.2612091 , 0.7552612 , 0.19051555, 0.61760926, 0.20619683,\n",
       "       0.5128214 , 0.24372411, 0.43076944, 0.27958155, 0.3592594 ,\n",
       "       0.40508524, 0.44725665, 0.5060243 , 0.36095524, 0.5865804 ,\n",
       "       0.29829958, 0.6594825 , 0.2511438 , 0.48905224, 0.46953386,\n",
       "       0.5989821 , 0.3752163 , 0.6900042 , 0.3043266 , 0.7698139 ,\n",
       "       0.24900024, 0.5427395 , 0.50916225, 0.6448762 , 0.41444728,\n",
       "       0.72937167, 0.34494254, 0.80114836, 0.2855965 , 0.5795653 ,\n",
       "       0.5612031 , 0.6605957 , 0.48530322, 0.72576857, 0.42653108,\n",
       "       0.7870482 , 0.36808896], dtype=float32)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import paddlehub as hub\r\n",
    "! hub install inferrence\r\n",
    "my_mnist = hub.Module(name=\"mnist_predict\")\r\n",
    "my_mnist.mnist_predict(\"2021-02-01_13-52-45_4.jpg\")\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "hub serving start -m mnist_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n",
      "b'{\"msg\":\"\",\"results\":\"{\\\\\"res\\\\\": \\\\\"[[0.27443912625312805, 0.7423494458198547, 0.33242034912109375, 0.6834733486175537, 0.3985059857368469, 0.6097073554992676, 0.45610281825065613, 0.5504773259162903, 0.5157178640365601, 0.4929301142692566, 0.47596144676208496, 0.48528337478637695, 0.5674338936805725, 0.39312106370925903, 0.6452239155769348, 0.3209387958049774, 0.7190911769866943, 0.2606605887413025, 0.4854966700077057, 0.4723811149597168, 0.5781634449958801, 0.37296435236930847, 0.6608476638793945, 0.2986185848712921, 0.7358506917953491, 0.235594242811203, 0.477549284696579, 0.4875773787498474, 0.5564754605293274, 0.3937729001045227, 0.62645024061203, 0.32508522272109985, 0.6913719177246094, 0.2691779136657715, 0.4612993597984314, 0.5198460221290588, 0.5147106051445007, 0.45262229442596436, 0.5600988268852234, 0.39764904975891113, 0.6072118878364563, 0.3483860194683075]]\\\\\"}\",\"status\":\"000\"}\\n'\n"
     ]
    }
   ],
   "source": [
    "import requests\r\n",
    "import json\r\n",
    "import cv2\r\n",
    "import base64\r\n",
    "\r\n",
    "def cv2_to_base64(image):\r\n",
    "    data = cv2.imencode('.jpg', image)[1]\r\n",
    "    return base64.b64encode(data.tobytes()).decode('utf-8')\r\n",
    "\r\n",
    "# 发送HTTP请求\r\n",
    "data = {'img_b64': cv2_to_base64(cv2.imread(\"2021-02-01_13-52-45_4.jpg\"))}\r\n",
    "headers = {\"Content-type\": \"application/json\"}\r\n",
    "url = \"http://0.0.0.0:8866/predict/mnist_predict\"\r\n",
    "r = requests.post(url=url, headers=headers, data=json.dumps(data))\r\n",
    "\r\n",
    "# 打印预测结果\r\n",
    "print(r)\r\n",
    "print(r.content)\r\n",
    "# print(r.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# !python resnet50.py"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PaddlePaddle 2.1.2 (Python 3.5)",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
